System and method for sensor replication for ensemble averaging in micro-electromechanical systems (MEMS)

ABSTRACT

A MEMs-based system (and method), includes a sensor array including at least two sensors providing a basis for ensemble averaging.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention generally relates to a method and apparatus forsensor replication, and more particularly to a method and apparatus forsensor replication for ensemble averaging in micro-electromechanicalsystems (MEMs).

2. Description of the Related Art

A MEMS-based servo positioning device, when enclosed within adisturbance free housing, is capable of producing high precisionmechanical displacement. Noise inherent in a position sensor that isembedded in a MEMS-based servo control system determines the precisionof the integrated system.

To improve the precision, noise generated in a position sensor [1-sigma(σ)] must be reduced. However, there is a fundamental limit tominimizing the sensor noise, and creative methods are required tocircumvent the performance limitation.

More generally, a sensing process requires a transducer and a signalconditioning method. A transduction process not only generates a usefulsignal, but inevitably produces a noise component, thus reducing theaccuracy of the sensing process. Using instrumentation-qualityelectronics, the total noise from a sensor can be kept to a minimum, butthe noise due to the transduction process cannot be eliminatedcompletely. Position sensing of an object can be derived from amultitude of transduction processes. Among non-contact transductionprocesses where frictionless movement is desired, optical, thermaland/or magnetic coupling effects can be employed.

FIG. 1 shows some exemplary elements of a single-axis position servocontrol system 100 including a MEMs positioning system 140. A positionsensor 1404 of the system 140 illustrated in this example is sensitiveto a location of an edge 1405 of a movable device 1403 designed to movewith respect to a stationary frame 1402.

The sensor voltage V(t) includes a noise component n(t). A servocontroller 125 produces a control signal U from the indicated positionerror signal (e.g., provided from an adder or summer unit (e.g., summingjunction) 120, and drives a power signal (e.g., typically an electriccurrent), via a driver 130, into an actuator 1401.

It is noted that an absolute position is provided from the MEMs-basedposition sensor 1404 back to an adder 115, which also receives sensornoise n(t), thereby to output a measured or indicated position V(t). Themeasured or indicated position V(t) combined with a target positionsignal, results in the measured or indicated position error signaloutput by the adder 120 to the servo controller 125, as described above.

The servo controller 125 and associated electronics (e.g., for measuringthe position, generating control signal U, etc.) are a subset of asystem controller 110. The system controller has a memory 150 (e.g., amemory bank) in which servo system parameters are stored during thepower-on operation of the servo control system 125.

FIG. 2 elaborates the parameters of a position sensor employed toestablish the advantages and merit of the present invention. In FIG. 2,a single sensor noise model 200 is shown receiving an input from aMEMs-based mechanical position sensor 225 based on the mechanical motion215 of a MEMs device. A noiseless (ideal) sensor output 2002 is shownbeing input to an adder 2003, which also receives a 10 MHz wide bandnoise (1-sigma=10*12.5 nm).

The adder 2003 provides an output to a lowpass filter 230 (e.g., havinga cutoff of 100 kHz), which in turn provides an analog output 240 of asingle MEMs position sensor 240 to a sampler 250. The sampler 250provides an output to a low pass filter (LPF) 260 which is a secondorder digital filter.

The LPF 260 provides a measured or indicated position (filtered) V(t) toa servo controller 205. The servo controller outputs a signal to anamplifier 210 to control the mechanical motion of the MEMs device.

It is noted that exemplarily the transducer is based on a thermalcoupling effect which is not the subject of the present invention. Thesensor dynamics 2001 are dominated by the thermal coupling effect whichhas a time constant of 50 μs, and is characterized by a first ordersystem.

The noise power spectrum measured after the 100 kHz second order analoglow-pass filter 230 contained a noise equivalent to 12.5 nm (1-sigma).The targeted displacement range of the position sensor is 100 μm. Inorder to demonstrate the invention through simulation and as mentionedabove, the sensor noise at the source is represented by a wide-band (10MHz) noise (10*12.5 nm 1-sigma).

In order to capture the effect of sensor noise in this application underrealistic operating conditions, a servo control system is required. Anindustry-proven proportional-integral-derivative (PID) positioning servosystem (e.g., servo controller 205 ) is employed for the MEMS-basedpositioning device. A characteristic PID controller transfer function,for example, in analog form, is represented by the following expression:Controller (Output/Input)=(k _(P) +k _(D) s+k _(I) /s)where gains k_(P), k_(D), and k_(I) are proportional, derivative andintegral gains, and ‘s’ is the Laplace transform operator. Theparameterization process to compute the gains is well-known in thefield. A control system designer would use a dynamic model of thescanner and would derive the gain values to achieve an optimum servocontroller design.

It is noted that if a MEMs-based sensor has too much noise, one coulduse a low pass filter (as described above), but such a low pass filterintroduces a phase lag.

FIGS. 3A-3B show the open loop transfer function of a characteristicMEMS-based position control system with a digital-PID controller. Acrossover frequency of 650 Hz is used in this study, as shown in FIG.3A. The controller is cascaded with a digital low pass filter (LPF) witha 4 kHz crossover frequency. FIG. 3B shows that the phase loss in thisregion degrades the settling performance.

FIG. 4 shows the position sensor output obtained after the 4 kHz LPF andestimated (through simulation) position of the MEMS device. It is notedthat the stand alone (i.e., without any servo action) sensor noisecomponent has 1-sigma of 12.5 nm.

However, after 4 kHz LPF and under closed loop servo conditions, thesensor output is reduced to 1-sigma of 4.6 nm (e.g., this component isreferred to as indicated or measured sensor output) because the MEMSsystem actually follows the sensor noise (an undesirable but necessaryeffect of the servo) at lower than crossover frequencies.

The low frequency noise following capability of a servo actuallyproduces physical motion (e.g., referred to as absolute position) andthe corresponding motion is detrimental to the precision centricperformance of a system. The estimated value of the absolute position is3.6 nm even though the noisy sensor output indicates 4.6 nm. It is notedthat, if an ideal sensor (i.e., zero noise component) was employed tomonitor the motion of the mechanical device, then it would measure 3.6nm.

The position accuracy of the servo control system can be improved byreducing the corner frequency “fc” of the low-pass filter shown in FIG.2. However, reducing the LPF corner frequency introduces additionalphase lag and penalizes the dynamic performance, such as settling time,unfavorably.

Thus, prior to the present invention, there has been no method or systemwhich shows how this limitation can be circumvented.

SUMMARY OF THE INVENTION

In view of the foregoing and other exemplary problems, drawbacks, anddisadvantages of the conventional methods and structures, an exemplaryfeature of the present invention is to provide a method and structure inwhich sensor noise is reduced without introducing additional phase lagand without penalizing the dynamic performance.

Another exemplary feature is to provide a system and method in whichsensor replication for ensemble averaging is performed.

In a first aspect of the present invention, a MEMs-based system,includes a sensor array including at least two sensors providing a basisfor ensemble averaging.

In a second aspect of the present invention, a method of reducing noisein a MEMs-based system, includes providing a sensor array including atleast two sensors, and ensemble averaging outputs of the sensor array.

In a third aspect of the present invention, a signal-bearing mediumtangibly embodying a program of machine-readable instructions executableby a digital processing apparatus to perform a method of reducing noisein a MEMs-based system, includes providing a sensor array including atleast two sensors, and ensemble averaging outputs of the sensor array.

In a fourth aspect of the present invention, a system for reducing noisein a MEMs-based system, includes a sensor array including at least twosensors, and a unit for ensemble averaging outputs of the sensor array.

With the unique and unobvious features of the present invention, thestrength of MEMS technology can be leveraged where a desired siliconfunction is replicated “N” number of times with reduced incrementalcost. While extra area and material for the MEMS substrate may beneeded, in the case of a position sensor, N of them are replicated tomeasure the same position variable, and by means of ensemble averagingthe effective sensor noise is reduced to (σ/N^(0.5)).

When the motion of an object is sensed using a common feature, forexample an edge, the ensemble average gives the mean position of theedge (e.g., encompassed by the replicated sensor array) with or withoutany rotation of the edge.

By taking the difference between the sensor output at extremumlocations, the rotation measure is extracted. In applications, forexample, in a storage device where the read or seek process may toleratemore positioning error in contrast to a write, erase or servoformatting/writing process, the number of sensors powered-up for thepurpose can be selected accordingly.

Finally, in systems where redundancy is required in case of single ormultiple failure, a replicated sensor array provides an extra layer ofprotection. In worst case situation where only one sensor with excessivenoise is available, the servo loop or filtering characteristics ismodified accordingly to recover the user data in a lower performancemode.

When appropriate, the sensor range can be segmented, and multiplesensors progressively covering each segment are engaged to produce asingle continuous output with superior noise properties. Segmentationproduces improved signal-to-noise ratio as well as sensor bandwidth.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other exemplary purposes, aspects and advantages willbe better understood from the following detailed description ofexemplary embodiments of the invention with reference to the drawings,in which:

FIG. 1 illustrates schematically a MEMS servo control system 100 with asingle position sensor;

FIG. 2 illustrates a sensor noise model 200 used in the analysis;

FIGS. 3A-3B illustrate a typical open loop transfer function of theposition control servo system;

FIG. 4 illustrates a servo position control performance with a singlesensor;

FIG. 5 illustrates a structure employing an ensemble averaging methodwith N-sensors;

FIG. 6 illustrates servo position control performance with a two-sensorconfiguration;

FIG. 7 illustrates servo position control performance with a four-sensorconfiguration;

FIG. 8 illustrates estimated and projected position error 1-sigmavalues;

FIG. 9 illustrates a structure employing a non-uniformly weightedensemble averaging method with N-sensors, according to the presentinvention;

FIG. 10 illustrates a structure 1000 including elements to achieve atradeoff between effective noise and total sensor power according to thepresent invention;

FIG. 11 illustrates a structure 1100 including simultaneous rotationmeasurement with linear ensemble averaged servo; and

FIG. 12 illustrates a structure 1200 including segmented sensors 1230for improved noise reduction.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS OF THE INVENTION

Referring now to the drawings, and more particularly to FIGS. 5-12,there are shown exemplary embodiments of the method and structuresaccording to the present invention.

Exemplary Embodiment

As mentioned above, the position accuracy of a servo control system canbe improved conventionally by reducing the corner frequency “fc” of alow-pass filter (e.g., as shown in FIG. 2).

However, reducing the LPF corner frequency introduces additional phaselag and penalizes the dynamic performance, such as settling time,unfavorably. The present invention overcomes this limitation using, forexample, ensemble averaging, as described below.

Theory of Ensemble Averaging:

The theory of ensemble averaging can be found in J. Bendat and A.Piersol, Random Data Analysis and Measurement Procedures, AWiley-Interscience Publication, 1986, p. 10.

Briefly, assume a single sensor output voltageV _(i)(x,t)=a*x(t)+n _(i)(t)   (1)where “i” denotes a sensor with noise n_(i)(t) which has a statisticalmean of 0 and a standard deviation of σ_(I). x(t) is the mechanicalposition at time instant “t”. “a” is the transducer gain assumed equalto all sensors.

If outputs of N sensors are added, then the total output voltage isgiven by:V _(T)(x,t)=N*(a*x(t))+sum (n _(i)(t))   (2)where the “summing” is carried over “N” sensors. It is well-known thatwhen the noise of each sensor is independent of each other and if σ₁=σ,then the standard deviation (std) of the term “sum (n_(i)(t))” can beshown as (SQRT in the following equation means square root):Std[sum (n _(i)(t))]=SQRT(N)*σ  (3)The instantaneous average of the total voltage can be shown to be:V _(AVE)(x,t)=[V _(T)(x,t)]/N=(a*x(t))+sum (n _(i)(t))/N   (4)Using the results of eq. (3), the standard deviation of the averagevoltage can be shown to be: $\begin{matrix}{\left. {{{Std}\left\lbrack {V_{AVE}\left( {X,t} \right)} \right\rbrack} = {{Std}\left\lbrack {{sum}\quad\left( {n_{i}(t)} \right)\text{/}N} \right)}} \right\rbrack = {{{{Std}\left\lbrack {{sum}\quad\left( {n_{i}(t)} \right)} \right\rbrack}\text{/}N} = {\sigma\text{/}{{SQRT}(N)}}}} & (5)\end{matrix}$

In the above case, all sensors are treated as having equal importanceand a uniform weighting of (1/N) is used.

The results can be generalized to include a non-uniformly-weightedensemble average, rather than a simple average represented by eq. (4).In this case, each sensor output is weighted by a normalized gain factorK_(i) (where sum (K_(i))=1) before the voltage is added to form theweighted average. Equations (4) and (5) then become:V _(W-AVE)(x,t)=sum[K _(i)*(a*x(t)+n _(i)(t))]  (6)andStd[V _(W-AVE)(x,t)]=SQRT (sum[(Ki*σ _(i))*(Ki*σ _(i))])   (7)If some sensors have distinctly different noise levels once the sensorgain “a” is matched, then the weighting term K_(i) can be chosen asfollows:K _(i) =L _(i) /L _(T)   (8)whereL _(i)=1/(σ_(i))² and L _(T)=sum (L _(i))   (9)Equations (8) and (9) help a logical criteria to allocate more weight tothe sensor outputs known to contain less noise.

Turning now to further details of the present invention, first it isnoted that a condition for the invention to be optimally effective inthe case of a position servo system is that the movable target (e.g., anedge, an optical mark, a magnetic field, etc.) displaces by an identicalamount along the axis of motion when multiple sensors are distributedover the target.

It is understood that the invention is applicable to any type oftransduction process (e.g., pressure, temperature, acceleration etc.),with or without a servo, where an identical physical variable is appliedto a replicated transducer array.

Further, it is noted that the present inventors have found that in aMEMs-based device, if one can use a number of relatively smallersensors, then results may be better than if a single large sensor isused.

FIG. 5 illustrates a system including the deployment of replicatedposition sensor array 510 in a MEMS device in which a uniformly weightedensemble averaging according to eq. (4) is demonstrated.

That is, FIG. 5 shows an N-tuple sensor model 500 with ensembleaveraging for enhanced servo precision. Reference numeral 520 shows themechanical motion of the MEMs device. Within the model 500, as shown,sensor noise 501 a, 501 b, etc. are summed with the sensor timeconstraints 502 a, 502 b, etc. (e.g., 50 μs) by an intermediate summer503 a, 503 b, etc.

Then, the output of the “N” identical (e.g., substantially identical orsimilar) sensors 510 are summed (e.g., by summer unit or summingjunction 504), and a uniform weighting factor of (1/N) (e.g., via gainamplifier 505 or the like) is applied. The weighting factor of “1/N” canalso be applied to each sensor output prior to the addition by summer504. Thereafter, the output of the amplifier 505 is input to an LPF 525,the output of the LPF (100 kHz) 525 is sampled by the sampler 530 andinput to the low pass filter 540. It is noted that as alluded to abovethe sensors need not be exactly identical for the superior results ofthe invention to be achieved.

If a single sensor of the replicated sensor array 510 has σ=12.5 nm,then the ensemble average will have 12.5/(SQRT(N)) as the standarddeviation. The arithmetic operations shown in FIG. 5 can be achievedeither in analog, digital or hybrid circuits with appropriate electronicelements and circuitry, and the automation of the ensemble averagingprocess is not elaborated in great detail.

FIG. 6 shows that an indicated position error of 3.1 nm and an absoluteposition error of 2.6 nm are attained when 2 sensors are employed forensemble averaging. Thus, FIG. 6 shows the servo position with theexemplary case of two position sensors, and a magnitude of improvementis clearly shown as compared, for example, to the plot of FIG. 4 (whichshows the results of using a single sensor). It is noted that thetheoretically projected values of 4.6/(2)^(0.5)=3.2 and3.6/(2)^(0.5)=2.6, are very close to what is expected when theequivalent sensor noise is reduced by the ensemble averaging processaccording to the present invention.

FIG. 7 corresponds to a case with N=4 (e.g., servo performance with theexemplary case of using four position sensors). As shown, the two noiseparameters are further reduced to 1.8 nm and 1.4 nm. Ideally, the sigmaestimates should have been 4.6/2=2.3 nm and 3.6/2=1.8 nm. However, sincethe estimates are made from limited sample lengths, a margin of errorcan be expected.

Thus, as shown in FIG. 7, with four sensors, the noise becomesprogressively better. Hence, if one can introduce four sensors, then onegains that much greater areal density capability, since the system canbe positioned that much more precisely.

FIG. 8 summarizes the trend and shows the performance of the sensors,and shows a core aspect of the present invention.

That is, the larger the number of replicated sensors in a system, thehigher is the potential to reduce the statistical positioning errorwithout compromising the servo performance.

The advantages of the invention are evident from FIG. 8 in which areplicated sensor array is shown to improve positioning accuracy whenensemble averaging is employed. It is noted that there may be adifference between the actual position error one would obtain and thetheoretical value of the position error (e.g., sigma over the squareroot of the number of sensors). Such difference may result from thesimulated value being a time domain-based estimate. Thus, as shown inFIG. 8, there may be some difference/variation between the theoreticalvalue and what one observes in the “real world” simulation.

In any event and as should be evident overall from FIG. 8, as moresensors are added the positioning error becomes smaller. The plot willhave a hyperbolic shape and at some point will level off (e.g., however,it will never cross the zero line). Thus, with four sensors the noisewill be decreased by a factor of 2, with nine sensors, the noise will bereduced by a factor of 3, with sixteen sensors, the noise will bereduced by a factor of 4, etc.

FIG. 9 extends the implementation of uniformly-weighted ensembleaveraging method by an extra step. That is FIG. 9 shows an N-tuplesensor model with weighted ensemble averaging for enhanced servoprecision.

Specifically, in applications where each sensor has similar gaincharacteristics, the noise level may vary depending on the MEMSconstruction detail. Under this condition, the sensor output can beweighted according to its reliability, for example, in proportion to itssignal-to-noise ratio (SNR). Relationships derived in eqs. (6), (7), (8)and (9) correspond to noise power-based weighting function. Hence, inFIG. 9, instead of giving equal weight to all the sensor outputs, theinvention recognizes that some sensors behave differently than others,and thus a gain term can be introduced depending upon the sensorbehavior. Hence, the sensors can be fine-tuned by using different gainsdepending upon their sensor performance and noise level.

Hence, in FIG. 9, one can imagine that with σ_(i) (the sensor noise),instead of adding a proportional number of σ_(i), then one can providemore weight to a “good” (in terms of performance) sensor and provideless weight to a “poor” sensor. Thus, optimal ratio combining can beused as discussed below.

The method of combining the sensor outputs is referred to as “OptimalRatio Combiner (ORC).” In applications where the transducer gain “a” issubject to variation, a calibration process is conducted to determinethe required “correction” gain for each sensor prior to the computationof the ensemble average. Hence, if the sensors have different gains,then it is possible to fine-tune the average computation process.

It is understood that the calibration and gain matching operations areeasily accomplished using elements of a digital computer and appropriatealgorithms.

Additionally, it is noted that calibration can be performed based ondimensional parameters and current, one could estimate the expectedgains. Hence, before shipping the sensor, one could perform a finecalibration on the manufacturing line, for example. As another example,one could have a high precision sensor which may draw a lot of power andbe undesirable for local use, but which could be used as a calibratorbefore the system is left to work on its own.

Returning to FIG. 9, there is shown a system including a replicatedposition sensor array 910 in a MEMS device in which weighted ensembleaveraging is performed. That is, FIG. 9 shows an N-tuple sensor model900 with weighted ensemble averaging for enhanced servo precision.Reference numeral 920 shows the mechanical motion of the MEMs device.Within the model 900, as shown, sensor noise 901 a, 90 1 b, etc. aresummed with the sensor dynamics (behavior) 902 a, 902 b, etc. by anintermediate summer 903 a, 903 b, etc.

Then, the output of the intermediate summers 903 a, 903 b, etc. areinput to a respective gain unit 905 a, 905 i, 905 n, etc. and then theoutputs of the gain units 905 a, 905, i, 905 n, etc. are input to asummer 904. Summer 904 provides an output to LPF 925.

It is noted that a disadvantage of replicating the sensor is that thepower consumption is increased proportional to “N”.

However, a tradeoff can be considered depending on the mode of use ofthe servo system. In the case of a storage application, the read processis more tolerant to positioning error than the write or erase process.Therefore, the number of sensors needed for a read servo operation canbe reduced, thereby saving power.

FIG. 10 shows a schematic of elements involved in an exemplary system1000 including a MEMs positioning system 1010, sensor power switch 1020,system controller 1030 and a sensor filtering and ensemble averagingmodule 1050.

System 1000 can utilize optimal ratio combining (e.g., using gainadditionally) in which gain (e.g., gain units 1040 a-1040 d) can be usedto adjust (penalize) accordingly sensors with greater or lesser noise.Thus, gain can be used.

Further, the sensors can be selectively powered down to save power,since not all of the sensors need to be energized (or at least energizedcompletely).

In FIG. 10, the system controller 1030 which has the knowledge of theread/write process can choose (via a signal to the sensor power switch1020) the best group of sensors (e.g., the two extreme sensors and anintermediate sensor) needed for a read operation, and power-down theremaining sensors that are not needed for the operation. (Generally,relatively more sensors are needed for write/erase operations as opposedto those needed for a read operation).

The corresponding information is concurrently transmitted to thefiltering and ensemble averaging module 1050 to account for the changein number of sensors.

Alternatively, the replicated sensor array provides built-in redundancyto the system. That is, in the case of an inadvertent failure of asensor, another sensor can be activated that would have beenpowered-down otherwise. Module 1050 issues a power optimized ensembleaveraged position signal 1060. Thus, in the system of FIG. 10, the gainand/or the sensors can be selectively adjusted/activated.

In a worst-case scenario, in a storage application where all sensorsexcept one have failed, a user alert is delivered through an appropriatecommunication protocol (e.g., visual and/or audio alarm unit 1070 ), andthe MEMS system is prepared to backup the data in the memory (not shownin FIG. 10). In order to achieve precision needed for a read process, alow-pass filter may be included in the servo loop and a corner frequencyof the LPF may be reduced drastically to meet the noise requirementwhile compromising the performance, such as access or settlingcharacteristics.

There are special configurations where the linear motion of a MEMSpositioner becomes distorted by angular rotation. Due to manufacturingasymmetry, for example, the linkages that help guide the motion may notbe etched as anticipated, and may induce a rotation component. Incertain cases, the members forming the MEMS structure may expandunevenly due to non-uniform thermal distribution of the silicon. Underthis condition, it may be necessary to infer the rotation of the movableplatform. The sensors at the extreme position can render the rotationinformation, as shown in FIG. 11.

More specifically, FIG. 11 illustrates a system 1100 including a MEMSdevice 1110 (having an edge 1115) in which ensemble averaging isperformed. In FIG. 11, sensor outputs are provided to a summer 1125, andthe sensor outputs by the two extreme sensors are also provided directlyto low pass filter 1135 a and 1135 n.

Summer 1125 provides a summed input signal to a gain unit 1130 whichoutputs an ensemble averaged linear position signal 1120. The lowpassfilters 1135 a and 1135 n provide an input to a summer 1140.

Then, the output of summer 1140, which is a signal representing arotation of the edge 1115, is provided to an analog-to-digital converter(ADC) 1160. The ADC 1160 outputs a signal to the system controller 1155which can access a memory 1150. System controller 1155 can also issue asignal to set the characteristics (e.g., set the corner frequencies,etc.) of the low pass filter(s). Thus, the low pass filter(s) can bedesirably programmed. For example, if high frequency rotationinformation is not needed, then the characteristics of the low passfilter(s) can be suitably programmed. An A/D converter also can beprovided for the output of the gain unit 1130.

Thus, in FIG. 11, the same sensors, while contributing to the ensembleaveraging process, can also contribute to rotation measurement. Therotation measurement noise level can be reduced by additional filteringsince this branch of the measurement is not part of a servo loop wherephase lag matters.

The sensor noise level and bandwidth may have a strong dependency on thetotal active range of a transducer. For example, a largerthermally-sensitive material will have a larger time constant and isliable to generate more thermal noise. The concept of replication can beused to make multiple sub-sensing elements with an overlapping region asshown in FIG. 12.

Thus, FIG. 12 shows the dilemma of wanting the sensors to have a goodlong-range detection capability and yet at the same time a goodsignal-to-noise ratio. However, in a situation where there is a singlesensor suffering from a problem of range versus noise, the presentinvention aims to overcome this problem as mentioned above.

More specifically, FIG. 12 illustrates system 1200 including a MEMsdevice having a mechanical motion 1210 and a segmented and replicatedsensor array 1220 including a plurality of sensors 1230.

For example, a sensor denoted by #n is actually segmented into aplurality (e.g., 5) shorter ranges n-a, n-b ..n-e. Each sensor segmentnow has shorter range with correspondingly improved noise and bandwidth.

The high output of a segment (e.g., say segment n-a denoted by V-H(n-a))is electronically switched (i.e., composed) to the low output of thenext segment (V-L(n-b)), thereby providing a longer range with improvednoise and bandwidth characteristics. The complexity of composing thesensor output can be somewhat simplified by augmenting the segmentedsensors with a single, large-range sensor so that during a high velocitymove, the coarse position and fine position information are readily madeavailable.

Thus, the system 1200 can cut down the range of detection of each sensorinto a plurality of portions/stages (e.g., na-ne shown in FIG. 12),thereby to minimize the noise. Hence, a shorter detection range portionof a sensor of interest can be selectively employed and switched.

Thus, with the unique and unobvious aspects of the present invention,ensemble averaging can be performed with (or without) a servo loop witha positioning system with an incoming phase loss, the invention candecrease the noise level. This is in complete contrast to a conventionallow pass filter which can cut down only the high frequency noise, but itdoes not affect the low frequency noise. In contrast, when the ensembleaveraging is performed according to the present invention, noise isreduced across the entire bandwidth. Hence, the present invention can beapplied to a MEMs device to improve positioning capability, withoutincurring a phase loss.

In addition to the hardware/software environment described above, adifferent aspect of the invention includes a computer-implemented methodfor performing the above method. As an example, this method may beimplemented in the particular environment discussed above.

Such a method may be implemented, for example, by operating a computer,as embodied by a digital data processing apparatus, to execute asequence of machine-readable instructions. These instructions may residein various types of signal-bearing media.

This signal-bearing media may include, for example, a RAM containedwithin a CPU, as represented by the fast-access storage for example.Alternatively, the instructions may be contained in anothersignal-bearing media, such as a memory 1150, or a magnetic data storageor CD-ROM diskette, directly or indirectly accessible by the CPU.

Whether contained in the memory, CD-ROM, diskette, the computer/CPU, orelsewhere, the instructions may be stored on a variety ofmachine-readable data storage media, such as DASD storage (e.g., aconventional “hard drive” or a RAID array), magnetic tape, electronicread-only memory (e.g., ROM, EPROM, or EEPROM), an optical storagedevice (e.g. CD-ROM, WORM, DVD, digital optical tape, etc.), paper“punch” cards, or other suitable signal-bearing media includingtransmission media such as digital and analog and communication linksand wireless. In an illustrative embodiment of the invention, themachine-readable instructions may comprise software object code,compiled from a language such as “C”, etc.

While the invention has been described in terms of several exemplaryembodiments, those skilled in the art will recognize that the inventioncan be practiced with modification within the spirit and scope of theappended claims.

Further, it is noted that, Applicant's intent is to encompassequivalents of all claim elements, even if amended later duringprosecution.

1. A MEMs-based system, comprising: a sensor array including at leasttwo sensors providing a basis for ensemble averaging.
 2. The system ofclaim 1, wherein said at least two sensors comprise at least tworeplicated sensors.
 3. The system of claim 1, further comprising: anoptimal ratio combiner that extends the ensemble averaging when thesensor array includes non-uniform noise statistics.
 4. The system ofclaim 1, further comprising: a system controller for controlling a powerof the sensor array such that the power is selectively adjusteddepending upon a desired precision.
 5. The system of claim 4, whereinthe system controller controls said power by limiting a number ofsensors activated for servo control.
 6. The system of claim 1, furthercomprising: means for detecting a sensor failure and for providing aredundancy against failure.
 7. The system of claim 1, wherein at leastone sensor of said at least two sensors is segmentable to selectivelyadjust a signal-to-noise ratio in relation to a desired range andcomplexity.
 8. The system of claim 1, further comprising: an alarm unitthat notifies a user of a failure of a predetermined number of said atleast two sensors.
 9. The system of claim 8, further comprising: alow-pass filter coupled to receive an output of said ensemble averaging;and a servo loop coupled to said low-pass filter, such that when saiduser is notified about a last remaining functioning sensor by said alarmunit, the servo loop allows for data backup with an increased level offiltering by said low-pass filter.
 10. The system of claim 1, whereinsaid at least two sensors operate based on a transduction processcomprising any of a pressure, temperature, and acceleration.
 11. Thesystem of claim 1, wherein said ensemble averaging is performed withservo control.
 12. The system of claim 1, wherein said ensembleaveraging is performed devoid of servo control.
 13. The system of claim1, wherein said at least two sensors are substantially identical. 14.The system of claim 13, further comprising: a summer that sums outputsof the substantially identical sensors; and a gain amplifier thatapplies a uniform weighting factor to one of an output of the summer andthe outputs of the substantially identical sensors.
 15. The system ofclaim 1, further comprising: a low-pass filter that receives an outputof the amplifier; a sampler that samples the output of the low passfilter; and a second low pass filter that receives the output of thesampler.
 16. The system of claim 1, wherein the ensemble averagingcomprises a weighted ensemble averaging such that the output of each ofsaid at least two sensors is weighted according to its signal-to-noiseratio (SNR).
 17. The system of claim 1, wherein said ensemble averagingcomprises a uniformly-weighted ensemble averaging such that equal weightis given to all outputs of said at least two sensors.
 18. The system ofclaim 16, further comprising: a gain unit that adjusts an output of asensor of said at least two sensors depending upon a behavior of saidsensor.
 19. The system of claim 1, further comprising: a gain unit that,depending upon a behavior of said at least two sensors, adjusts any ofthe outputs of said at least two sensors by using different gainsdepending upon a performance and a noise level of said any of said atleast two sensors.
 20. The system of claim 1, further comprising: anoptimal ratio combiner that combines the sensor outputs: and acalibrator that calibrates said sensors to determine a requiredcorrection gain for each sensor prior to computation of the ensembleaverage.
 21. The system of claim 1, further comprising: a systemcontroller that selectively controls an activation and deactivation of asensor of said at least two sensors.
 22. The system of claim 21, whereinthe system controller controls said at least two sensors to select abest sensor or a best group of sensors for a predetermined operation,and powers-down remaining sensors that are not needed for thepredetermined operation.
 23. The system of claim 22, further comprising:a filtering and ensemble averaging module, wherein information regardingthe sensors selected by said system controller, is concurrentlytransmitted from said system controller to the filtering and ensembleaveraging module to account for a change in number of sensors.
 24. Thesystem of claim 22, wherein in a case of inadvertent failure of anactivated sensor, said system controller activates a second sensor whichis powered-down.
 25. The system of claim 22, wherein first and secondsensors of said at least two sensors are located at two extremepositions of said MEMs device, and provide rotation information of acomponent of said MEMs device.
 26. The system of claim 25, furthercomprising: first and second low pass filters for receiving outputs fromthe first and second sensors, said first and second lowpass filtersproviding an input to a summer, said summer receiving a gain signalhaving been applied to sensor outputs other than those from the firstand second sensors and an ensemble averaged linear position signal,wherein the output of summer is combined with a signal representing arotation of an edge of said component of said MEMs device, saidcomponent comprising a movable platform.
 27. The system of claim 22,wherein same sensors of said at least two sensors contributing to theensemble averaging also contribute to rotation measurement of acomponent of said MEMs device.
 28. The system of claim 1, wherein asensor of said at least two sensors includes multiple sub-sensingelements with an overlapping region.
 29. The system of claim 1, whereinsaid at least two sensors comprise a segmented and replicated sensorarray.
 30. The system of claim 1, wherein the ensemble averaging reducesnoise across an entire bandwidth of said at least two sensors.
 31. Thesystem of claim 1, further comprising: a system controller controllingsaid at least two sensors; and an alarm unit that notifies any of saidsystem controller and a user that a predetermined number of sensors ofsaid at least two sensors have failed, and initiates a backup of datacollected by said at least two sensors.
 32. A method of reducing noisein a MEMs-based system, comprising: providing a sensor array includingat least two sensors; and ensemble averaging outputs of said sensorarray.
 33. The method of claim 32, wherein said at least two sensors arereplicated to measure a same position variable, and wherein saidensemble averaging reduces an effective sensor noise to (σ/N^(0.5)),where N is a number of sensors of said at least two sensors.
 34. Themethod of claim 32, further comprising: sensing a motion of an objectusing a common feature of said object, said ensemble average providing amean position of the common feature with rotation of the common feature.35. The method of claim 32, further comprising: sensing a motion of anobject using a common feature of said object, said ensemble averageproviding a mean position of the common feature without any rotation ofthe common feature.
 36. The method of claim 32, further comprising:taking a difference between output of sensors positioned at extremelocations, thereby to extract a rotation measurement.
 37. The method ofclaim 32, further comprising: selectively powering-up a number ofsensors for the ensemble averaging.
 38. The method of claim 32, furthercomprising: modifying one of a servo loop and a filtering characteristicto recover a user data in a lower performance mode.
 39. The method ofclaim 32, further comprising: segmenting a sensor range of said sensorarray into a plurality of segments; and engaging multiple sensorsprogressively covering each segment to produce a single continuousoutput with predetermined noise properties.
 40. A signal-bearing mediumtangibly embodying a program of machine-readable instructions executableby a digital processing apparatus to perform a method of reducing noisein a MEMs-based system, comprising: providing a sensor array includingat least two sensors; and ensemble averaging outputs of said sensorarray.
 41. A system for reducing noise in a MEMs-based system,comprising: a sensor array including at least two sensors; and means forensemble averaging outputs of said sensor array.